Seungtaek Jeong
Papers
1
Total Citations
35
H-Index
1
About
Seungtaek Jeong is a leading researcher in atmospheric remote sensing, with a focus on advancing the estimation of aerosol optical properties from satellite data. His primary research areas include the development of deep neural networks and machine learning models to improve the retrieval of aerosol optical depth (AOD), a critical parameter for understanding air quality and climate dynamics. In his highly cited 2021 study, Jeong introduced a novel deep learning approach that significantly outperforms traditional physical models in estimating hourly AOD from GOCI geostationary satellite data. This work addresses a long-standing challenge: accurately separating aerosol reflectance from surface reflectance over land, enabling more precise spatiotemporal monitoring of aerosols. With 35 citations, this paper underscores his impact on the field, offering a robust alternative to conventional methods. Jeong’s contributions are particularly notable for bridging the gap between advanced computational techniques and operational satellite remote sensing, providing tools that enhance real-time environmental monitoring. His research is essential for scientists and students working on air pollution, climate modeling, and satellite data analysis, marking him as a key innovator in the integration of AI with geoscience.
Research Focus
Key Achievements
Top Papers
- 1